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---
layout: global
type: "page singular"
title: MLlib
description: MLlib is Apache Spark's scalable machine learning library, with APIs in Java, Scala, Python, and R.
subproject: MLlib
---
<div class="jumbotron">
<b>MLlib</b> is Apache Spark's scalable machine learning library.
</div>
<div class="row row-padded">
<div class="col-md-7 col-sm-7">
<h2>Ease of Use</h2>
<p class="lead">
Usable in Java, Scala, Python, and R.
</p>
<p>
MLlib fits into <a href="{{site.baseurl}}/">Spark</a>'s
APIs and interoperates with <a href="http://www.numpy.org">NumPy</a>
in Python (as of Spark 0.9) and R libraries (as of Spark 1.5).
You can use any Hadoop data source (e.g. HDFS, HBase, or local files), making it
easy to plug into Hadoop workflows.
</p>
</div>
<div class="col-md-5 col-sm-5 col-padded-top col-center">
<div style="margin-top: 15px; text-align: left; display: inline-block;">
<div class="code">
data = spark.read.format(<span class="string">"libsvm"</span>)\<br/>
.load(<span class="string">"hdfs://..."</span>)<br/>
<br/>
model = <span class="sparkop">KMeans</span>(data, k=10)
</div>
<div class="caption">Calling MLlib in Python</div>
</div>
</div>
</div>
<div class="row row-padded">
<div class="col-md-7 col-sm-7">
<h2>Performance</h2>
<p class="lead">
High-quality algorithms, 100x faster than MapReduce.
</p>
<p>
Spark excels at iterative computation, enabling MLlib to run fast.
At the same time, we care about algorithmic performance:
MLlib contains high-quality algorithms that leverage iteration, and
can yield better results than the one-pass approximations sometimes used on MapReduce.
</p>
</div>
<div class="col-md-5 col-sm-5 col-padded-top col-center">
<div style="width: 100%; max-width: 272px; display: inline-block; text-align: center;">
<img src="{{site.baseurl}}/images/logistic-regression.png" style="width: 100%; max-width: 250px;">
<div class="caption" style="min-width: 272px;">Logistic regression in Hadoop and Spark</div>
</div>
</div>
</div>
<div class="row row-padded" style="margin-bottom: 15px;">
<div class="col-md-7 col-sm-7">
<h2>Easy to Deploy</h2>
<p class="lead">
Runs on existing Hadoop clusters and data.
</p>
<p>
If you have a Hadoop 2 cluster, you can run Spark and MLlib without any pre-installation.
Otherwise, Spark is easy to run <a href="{{site.baseurl}}/docs/latest/spark-standalone.html">standalone</a>
or on <a href="{{site.baseurl}}/docs/latest/ec2-scripts.html">EC2</a> or <a href="http://mesos.apache.org">Mesos</a>.
You can read from <a href="http://hadoop.apache.org/docs/stable/hadoop-project-dist/hadoop-hdfs/HdfsUserGuide.html">HDFS</a>, <a href="http://hbase.apache.org">HBase</a>, or any Hadoop data source.
</p>
</div>
<div class="col-md-5 col-sm-5 col-padded-top col-center">
<img src="{{site.baseurl}}/images/hadoop.jpg" style="width: 100%; max-width: 280px;">
</div>
</div>
<div class="row">
<div class="col-md-4 col-padded">
<h3>Algorithms</h3>
<p>
MLlib contains many algorithms and utilities, including:
</p>
<ul class="list-narrow">
<li>Classification: logistic regression, naive Bayes,...</li>
<li>Regression: generalized linear regression, isotonic regression,...</li>
<li>Decision trees, random forests, and gradient-boosted trees</li>
<li>Recommendation: alternating least squares (ALS)</li>
<li>Clustering: K-means, Gaussian mixtures (GMMs),...</li>
<li>Topic modeling: latent Dirichlet allocation (LDA)</li>
<li>Feature transformations: standardization, normalization, hashing,...</li>
<li>Model evaluation and hyper-parameter tuning</li>
<li>ML Pipeline construction</li>
<li>ML persistence: saving and loading models and Pipelines</li>
<li>Survival analysis: accelerated failure time model</li>
<li>Frequent itemset and sequential pattern mining: FP-growth, association rules, PrefixSpan</li>
<li>Distributed linear algebra: singular value decomposition (SVD), principal component analysis (PCA),...</li>
<li>Statistics: summary statistics, hypothesis testing,...</li>
</ul>
<p>Refer to the <a href="{{site.baseurl}}/docs/latest/mllib-guide.html">MLlib guide</a> for usage examples.</p>
</div>
<div class="col-md-4 col-padded">
<h3>Community</h3>
<p>
MLlib is developed as part of the Apache Spark project. It thus gets
tested and updated with each Spark release.
</p>
<p>
If you have questions about the library, ask on the
<a href="{{site.baseurl}}/community.html#mailing-lists">Spark mailing lists</a>.
</p>
<p>
MLlib is still a rapidly growing project and welcomes contributions. If you'd like to submit an algorithm to MLlib,
read <a href="https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark">how to
contribute to Spark</a> and send us a patch!
</p>
</div>
<div class="col-md-4 col-padded">
<h3>Getting Started</h3>
<p>
To get started with MLlib:
</p>
<ul class="list-narrow">
<li><a href="{{site.baseurl}}/downloads.html">Download Spark</a>. MLlib is included as a module.</li>
<li>Read the <a href="{{site.baseurl}}/docs/latest/mllib-guide.html">MLlib guide</a>, which includes
various usage examples.</li>
<li>Learn how to <a href="{{site.baseurl}}/docs/latest/#launching-on-a-cluster">deploy</a> Spark on a cluster
if you'd like to run in distributed mode. You can also run locally on a multicore machine
without any setup.
</li>
</ul>
</div>
</div>
<div class="row">
<div class="col-sm-12 col-center">
<a href="{{site.baseurl}}/downloads.html" class="btn btn-success btn-lg btn-multiline">
Download Apache Spark<br/><span class="small">Includes MLlib</span>
</a>
</div>
</div>
|
